Challenge Follow-Up: Brandon McCarthy & HR/FB
Last week, I issued a challenge. If you’re the type to call a pitcher who sports an inflated HR/FB rate “homer-prone”, I asked you to prove that this was not merely bad luck, but skill-related (or a lack of skill). I have been writing about fantasy baseball for 7+ years now and nothing bothers me more than when a definitive claim is made with no supporting evidence. There is a difference between “Pitcher X is homer-prone” and “Pitcher X has been homer-prone”. The former suggests an inherent lack of skill in keeping fly balls in the park, while the latter makes no such commentary on the pitcher’s home run avoidance skills, but merely describes what has happened.
Since it’s clear that we really don’t know for sure what leads to home run suppression skills or a lack thereof, at this point, only the aforementioned latter description seems appropriate in my mind. So Brandon McCarthy’s early struggles with gopheritis, which has carried over from last year’s issues, was what motivated me to challenge you, as I figured the homer-prone label would start popping up everywhere.
As I had hoped, my challenge sparked some great discussion. So today I’ll highlight some of the more interesting comments. Even if we can’t come up with definitive answers now, we will be better prepared for what to look for and research once better data is available.
Several commenters asked about the distance of a pitcher’s fly balls allowed. I analyze the hitter side and even developed an equation to predict HR/FB rate. I had completely forgotten that I actually researched a pitcher’s average fly ball distance and posted about it back in January!
I first calculated the year-over-year correlation of a pitcher’s batted ball distance. It came out to .308, which is worse than for hitters, but still meaningful. Then I took things a step further, calculating the correlation between distance and HR/FB rate, which sat at .442. Again, significantly worse than for hitters, but still something. Last, I tried to devise an xHR/FB rate for pitchers like I did for hitters, using the same three components. Unfortunately, the R-squared was just .276.
Anyway, now we know that a pitcher’s batted ball distance again does convey something, but less something than for hitters. Let’s check out McCarthy’s distance trend (through his second start):

So McCarthy’s HR/FB rate has pretty much trended along with his batted ball distance allowed. This doesn’t tell us anything of course. We’re trying to figure out whether that batted ball distance allowed is a fluke/bad luck and if not, why is it suddenly so inflated?
Now let’s get to some comments.
jdbolick says:
I also looked at heatmaps under the hypothesis that HR/FB% could be related to the percentage of pitches thrown in the middle of the strike zone, but noticed something else instead. What I saw, and what is presumably related to the above point regarding fly-ball percentages, is that pitchers who throw a high percentage of pitches in the bottom third of the strike zone have high HR/FB percentages whereas pitchers who throw a high percentage of pitches in the upper third of the strike zone have low HR/FB percentages.
When Brandon McCarthy threw up in the zone more than most, he had a HR/FB% better than most. When he changed to throwing down in the zone more than most, he had a HR/FB% worse than most.
Only a handful of examples were provided and no full study performed due to how time-consuming collecting the data is. But it’s interesting to note and certainly intriguing enough to look into further. There have been conflicting studies on whether ground ball pitchers allow higher or lower HR/FB rates and selection/survivor bias plays a role.
Rowen says:
Because he’s a sinkballer.
Pitchers who feature the sinker as their primary pitch typically have a high HR/FB%. Look at Edison Volquez for example—in 2014 his sinker had an HR/FB of 17.2%. Not very unlike McCarthy’s, which was 17.7% in 2014. Masterson was 15.2% in 2013 but inflated to 25% in 2014 due to a massive reduction in the velocity of his sinker.
Sinkballers let up more homeruns.
It is absolutely, statistically true that pitchers who feature the sinker as their primary pitch have a higher HR/FB% when compared to all other pitchers.
Again, only a couple of cherry picked examples, but this seems to relate to jdbolick’s comment above. You would assume the sinker guys would be the same as those pitching more to the bottom of the zone. Although Rowen claims it is “statistically true”, no actual evidence was provided. This was the entire point of my challenge! He may very well be right, but I need to see some data that supports such a conclusion.
Eno Sarris says:
I found no correlation between zone% or sinker usage and HR/FB.
I found a tiny correlation between o-swing and HR/fB R-Squared: 0.0524017.
Finally, some facts! What Eno found looks to have eliminated the sinker usage theory that Rowen was so confident was true.
But then Rowen was back, this time with a chart. No description of exactly what his study involved, and whether it was the pitcher’s career HR/FB rate and sinker usage or what. And it was only a handful of pitchers. But it certainly seems like a positive trend:

This chart seems to disagree with what Eno found, so I’m curious what each looked at.
I also received an email from Zachary Smith, who I quickly learned develops his own projections and is a true data ninja. Perfect!
He told me:
I’ve actually done a few thousand hours of research on this topic and many others as I do my own projections system yearly. It turns out a pitcher does have some control over their HR/FB rate, but it’s not directly related to the “quality of their stuff”.
HR/FB rate is related to two factors: first, one’s FB%; secondly, one’s true infield fly ball percentage. We all know that IFFB% is actually not the percentage of infield fly balls; rather, it is the percentage of fly balls that are infield fly balls. By true IFFB% I mean one simply goes one step further and multiplies FB% and IFFB% to arrive at the number of balls in play that are infield fly balls.
It turns out one’s HR/FB rate = – 0.155*TrueIFFB% – 0.0585*FB% + 0.12
…the one aspect of batted ball distribution that a pitcher does control is what I call the “wedge”.
The “wedge” I refer to is the trajectory of the ball off the bat as viewed from the side. It turns out that a pitcher can tilt this wedge upwards or downwards and also widen the range of probable distributions or more tightly control that distribution. I’m still researching how this happens, but it is a repeatable yearly skill that is revealed in one’s FB% and IFFB% which, at the moment, is what we care about.
What one is trying to limit is hard hit fly balls. When one tilts the wedge upwards the hard hit fly balls move into a slot of lower probability on this wedge. This stacks up with evidence that has been accrued in the past showing that fly ball pitchers have slightly lower HR/FB rates. Further, pitchers who generate a lot of infield fly balls show that they have either tilted the wedge much higher or have widened the outcome of distributions, both factors that positively affect HR/FB.
P.S. McCarthy, while sporting a league average 38% fly ball percentage so far this year has sported below average fly ball rates (mid twenties) the last two years and below average infield fly ball rates (league average is about 9.6%) of 8.7% and 6.5% yielding well below average true IFFB% rates of 2.4% and 1.6% respectively. McCarthy deserves some of his inflated HR/FB% but, as you’ll notice, the most extreme HR/FB rate possible according to my regression line is 12% and that’s if you throw 99.999% ground balls and generate no pop ups. McCarthy may have earned some of his poor luck, but certainly not the lion’s share of it.
So what Zachary has found in his deep research is that fly ball pitchers who induce lots of pop-ups have slightly lower HR/FB rates. I could swear I read one study that concluded the opposite, though the others all agreed with what Zachary found. Still, McCarthy’s batted ball distribution certainly doesn’t justify a HR/FB rate so significantly above the league average.
So while it is seemingly far too time-consuming and difficult to do the research with the data currently available, we have some avenues to go down. We should look at pitch mix and location, velocity I guess, and batted ball distribution. Eventually, we should know a lot more about which kind of pitchers could suppress homers per fly ball and who might suffer from an inflated mark.
Mike Podhorzer is the founder of ProjectingX IQ, an advanced fantasy baseball analytics platform that transforms projection data and in-season performance signals into actionable intelligence. He is the 2015 Fantasy Sports Writers Association Baseball Writer of the Year and three-time Tout Wars champion. He is the author of the eBook Projecting X 2.0: How to Forecast Baseball Player Performance, which teaches you how to project players yourself. Follow Mike on X@MikePodhorzer and contact him via email.
Tucked in an article for The Hardball Times, Matthew Murphy provides some evidence for a correlation between GB% and HR/FB rate.
http://www.hardballtimes.com/are-groundball-pitchers-overrated/
Intuitively it makes sense that pitchers with a high GB% would have a lower IFFB% and thus a higher HR/FB rate.
Yeah, this makes sense to me, and seems to follow the data.
Gah! That’s where it was. I knew I was looking for this.
Except there’s that whole selection/survivor bias going on here where FB pitchers that have high HR/FB% are not going to be in MLB very long if they get there at all.
Excellent by all. More data and thoughts to come I’m sure. I wonder if Rowen mis-spoke when he said sinkerballers let up more home runs. Do they let up more home runs or have a higher HR/FB%? Just wondering. Also, don’t hitters, lefties in particular hit more home runs when the ball is down in the zone? I say this with absolutely no data except the eye test. How often have we seen Ken Griffey Jr. smash a ball low and in? Again eye test. But that’s my contribution. Ha!
Sorry, but thinking more. Does back-spin on the ball off the bat have anything to do with this? Is it harder to put back-spin on a ball up in the zone? I’m sorry I have no skills to add to the research. But I’m just thinking out loud where you guys might be able to do the research.
I’m looking forward to reading more.
It is going to come out that after all this research the true answer is that McCarthy is tipping pitchers. 🙂
Speaking of McCarthy this is what Rotoworld said about him : “McCarthy’s career K/9 is 6.6, so it’s probably not logical to expect him to keep up this breakneck pace.” That is surprising from a site like Rotoworld.
They conveniently left out the fact that his fastball velocity sits at a career high. Their analysis is beyond awful.
also ignores that his K rate jumped significantly last year, and that he mostly maintained it for the whole year (higher K/9 2nd half compared to 1st), and that his swinging strike rate is way up in 2015. Typical Matthew Pouliot analysis.
What site would you recommend for better analysis that provides updates as fast as Rotoworld?
I use RW strictly for news. Then come here for the analysis!
A company man!
Does Fangraphs have instant analysis?
We’re pretty quick with big news, but obviously not instant like RW. You can Tweet or email me though and I respond quickly.
Thanks. The big stuff and in depth analysis is here, but often I need to make quick decisions. For example, if I did not know about Mccarthy’s increased velocity, increased K etc. he would be the 1st guy I dropped in one of my leagues. There are plenty of guys where I would of taken the analysis to heart.
I completely disagree with your attack on the rotowire guy. First off, this is fangraphs. A pitcher is far more likely to revert to career norms (and league norms) than he is to maintain elevated levels. Maybe Rotowire guy should’ve used a weighted average to describe McCarthy’s K-Rate.
But, you say, he throws so much harder now. Guys, we’re dealing with a sample size of three games. Go back to last year, you can find a couple spurts of 2 or 3 games where McCarthy was touching 95 on his FB/SI and his K% was 21%. You can cherry pick three game spurts however you’d like. This year, it’s 34%. His career average? 17%. You can’t just proclaim that because these are the first three games of the season, they are any different from the middle three games of last year’s season.
Considering last year was 21% (even with increased velocity), I think it’s a lot better to say McCarthy is more likely to return to the career average of 17% than to maintain his current pace of 34%.
Never fault someone for erring on the side of regression, imo.
McCarthy’s velocity was up last year too. The argument for regression using career K/9 is poor because he is a different pitcher.
Kris, did you actually look at McCarthy’s numbers from last year before talking about them? 200 innings pitched, and a full 2 MPH over his career fastball. And he’s even found another tick on it this year. And a correlating bump in SwStr% that has held into this year, plus a decreased BB%.
He’s not the same pitcher.
This is my exact point, obviously. As I clearly stated, McCarthy’s velocity was up last year and yet, he managed only a 21% K-Rate over 200 IP.
One argument is that his velocity is up further this year, another 1MPH, to which I said, if you cherry pick numbers from last year, you could say something very similar. Three game samples can be cherry picked from basically anywhere.
With all that said:
K-Rate:
Last Year: 21%
Career: 17%
2015: 35%
You can bust on the rotowire guy all you want but you should definitely agree with the spirit of what he’s saying. McCarthy’s 2015 K% should be regressed to (at a minimum) last year’s 21% number where he showed the increased velocity.
Furthermore, it is incredibly rare that a pitcher adds velocity this late in his career. So while McCarthy maintained it all last year, and into this year, it’s still *much* safer simply regressing McCarthy to his career norms than it is to declare that *everything is changed and he is the 1-in-100 case*
As I said, it’s much safer to err on the side of regression. I also don’t understand how I couldn’t have looked at McCarthy’s numbers last given I quoted his velocity and k% in the response…
Saying that his k% is more likely to regressing to his career 17% than stay at 35% does not help much. What is his expected k%.
It is like me saying that Nelson Cruz is more likely to hit 40 home runs this year than the 100 he is on pace for. It is true, but how much does that help?
Let’s stop mentioning Rotowire. It’s Rotoworld with the poor analysis. You are certainly not wrong about anything you said about velocity gains and regressing k-rate. The issue is to make absolutely not mention of the velocity gains in the blurb when forecasting his future strikeout rate. It’s just lazy analysis.
*sigh* I provided more than “a handful of examples,” Mike, and in case you’re wondering the data continues to hold up over a substantially larger sample. I have yet to complete all 191, but what I posted about location in the strike zone affecting HR/FB rate is unquestionably true. If you intend to have more of these challenges in the future, I would suggest being less dismissive when people provide insights and evidence that you weren’t expecting.
You provided about 15 examples. That’s nowhere near the population sample size needed to conclude anything with any sort of confidence. I wasn’t dismissive, as I noted that it was interesting and worth looking into further.
Haven’t you heard Mike? jdbolick is never wrong.
Mike, I think you’re being overly harsh.
Looking at this, and I think I mentioned this, but this guy is using SI% meanwhile, Eno almost certainly used FT/SI whatever. There’s no correlation if you’re using FT, but there looks to be a correlation when you’re looking at just SI. As shown in the guy’s example, there’s only like 20 guys who throw *sinkers*
What I think would be awesome, is if you looked at Spin Angle or Movement and checked that for correlation, because as I said, relying on pitch groupings is … rough.
All of this stuff is available via MLB’s pitchfx. It’s just a matter of running the query.
I was playing with pitch f/x data to try and get more answers on this. For McCarthy, his homers on sinkers were on pitches with less spin and less drop. The differences looked significant to me, but I don’t have the data set or the know-how to confirm if they are significant.
And of course, the question remains: how often do pitchers throw pitches that deviate from their usual spin/movement by that amount, and how often do they get punished for it.
It’s not clear that McCarthy is throwing more bad pitches than normal. But it does seem to me that the home runs were given up on worse pitches than the non-home runs. Shocker. 🙂
This is interesting. And perhaps there is a correlation between spin/ drop and HR/FB ratio. No idea where to get this data from though!
I wanted to quibble with this:
So McCarthy’s HR/FB rate has pretty much trended along with his batted ball distance allowed. This doesn’t tell us anything of course. We’re trying to figure out whether that batted ball distance allowed is a fluke/bad luck and if not, why is it suddenly so inflated?
Sure it tells us something. There are different kinds of bad luck in play here. He could be getting unlucky by allowing a bunch of weak fly balls that just happen to go to the shortest part of the ballpark he’s pitching in, or he could be getting unlucky in that he’s getting a greater percentage of his “mistake” pitches hit hard, resulting in a greater batted ball distance than you would expect for a pitcher with his skills. The fact that his batted ball distance is elevated this year tells us that it’s more the second type of unlucky than the first type. That’s useful to know.
I stand corrected. I agree with you and this is useful to know.
Thinking about Tom Glavine and his preposterously low and consistant HR/FB rates for a really, really long time. A majority of his flyballs allowed were opposite field and opposite field flyballs are less likely to be HRs. What factors lead to causing more opposite field FBs? Just pitching outside over and over or was it something to do with repetoire too? I.e. changeup heavy, etc?
In a game like baseball with so many variables at play all at once you will never be able to PROVE any of these theories, you can be right 99 times out of a hundred all it takes is one example to disprove the theory, and there always is at least one outlier. So what you are left with is, is there any statistical significance. This whole challenge about what you can PROVE was ridiculous right from the jump.
Boo to you Kelo. It’s not ridiculous! It’s fun. And does being wrong 1% of the time dis-prove anything?
Repeating…..Booooo!!!
The point was that the author is very smug about what people can PROVE, where in reality you can PROVE none of it, you may be able to state that something has statistical significance but nothing beyond that, the theory must hold up every time out there is a problem with the theory.
Assumption: When people refer to a “Fly ball Pitcher” they mean a pitcher who gets a large percentage of outs (and batted balls in play) as fly balls.
Thinktank’s extrapolation: Fly ball pitchers limit home runs better because their HR/FB is lower
Fallacy identified: Getting lots of outs (and batted balls in play) as fly balls inflates the denominator. A “Fly ball Pitcher” could give up the exact same number of HR as any other pitcher and have a much lower HR/FB rate.
Conclusion: HR/FB rate is not a useful statistic.
What am I missing here?
Isn’t the assumption that more fly balls equals more home runs? Do fly ball pitchers have lower hr/fb rates? Is that some of the belief here? I’m not beating on your thought. I’m just asking more questions.
Also……SPIN was talked about in the third entry here. Glad someone picked up on it. Although I asked about back-spin it still is relevant. Are pitches down in the zone easier to put back-spin on? Or do you get more back-spin on pitches lower in the zone?
@Anon No one is interested in projecting future HR/FB rate for the sake of knowing future HR/FB rate. We try to predict future HR/IP. We do this by taking a the estimate of a pitcher’s true HR/FB skill and applying it to his predicting FB/out rate. So of course a flyball pitcher is going to allow more actual HRs than a GB pitcher, if they have the same rate. Who is saying otherwise?